Identification of Pathologic and Prognostic Genes in Prostate Cancer Based on Database Mining

Abstract

Background: Prostate cancer (PCa) is an epithelial malignant tumor that occurs in the urinary system with high incidence and is the second most common cancer among men in the world. Thus, it is important to screen out potential key biomarkers for the pathogenesis and prognosis of PCa. The present study aimed to identify potential biomarkers to reveal the underlying molecular mechanisms.Methods: Differentially expressed genes (DEGs) between PCa tissues and matched normal tissues from The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset were screened out by R software. Weighted gene co-expression network analysis was performed primarily to identify statistically significant genes for clinical manifestations. Protein–protein interaction (PPI) network analysis and network screening were performed based on the STRING database in conjunction with Cytoscape software. Hub genes were then screened out by Cytoscape in conjunction with stepwise algorithm and multivariate Cox regression analysis to construct a risk model. Gene expression in different clinical manifestations and survival analysis correlated with the expression of hub genes were performed. Moreover, the protein expression of hub genes was validated by the Human Protein Atlas database.Results: A total of 1,621 DEGs (870 downregulated genes and 751 upregulated genes) were identified from the TCGA-PRAD dataset. Eight prognostic genes [BUB1, KIF2C, CCNA2, CDC20, CCNB2, PBK, RRM2, and CDC45] and four hub genes (BUB1, KIF2C, CDC20, and PBK) potentially correlated with the pathogenesis of PCa were identified. A prognostic model with good predictive power for survival was constructed and was validated by the dataset in GSE21032. The survival analysis demonstrated that the expression of RRM2 was statistically significant to the prognosis of PCa, indicating that RRM2 may potentially play an important role in the PCa progression.Conclusion: The present study implied that RRM2 was associated with prognosis and could be used as a potential therapeutic target for PCa clinical treatment.

Endocrine Disrupting Chemicals Influence Hub Genes Associated with Aggressive Prostate Cancer

“…Higher levels of CCNB1 in PCa cells may have a beneficial effect on polyploidy and a prognostic biomarker for chemotherapy [ 70 ]. CDK1 controls mitochondrial metabolism for bioenergetics needed for tumor cell survival, and overexpression of CDK1 is associated with poor prognosis and metastasis in PCa [ 71 ]. CNB2 particularly binds CDC2 to improve cell migration which is connected to the development of CRPC and also plays a critical part in transforming growth factor beta-mediated cell cycle control [ 70 , 71 ].…”

“…CDK1 controls mitochondrial metabolism for bioenergetics needed for tumor cell survival, and overexpression of CDK1 is associated with poor prognosis and metastasis in PCa [ 71 ]. CNB2 particularly binds CDC2 to improve cell migration which is connected to the development of CRPC and also plays a critical part in transforming growth factor beta-mediated cell cycle control [ 70 , 71 ]. UBE2C is key for the progression of PCa, and the level of UBE2C is important to predicting the prognosis of patients [ 72 ].…”